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Course Outline
Deep Learning vs. Machine Learning vs. Other Methods
- When to apply Deep Learning
- Limits of Deep Learning
- Comparing accuracy and cost across different methods
Overview of Methods
- Nets and Layers
- Forward and Backward passes: The essential computations of layered compositional models.
- Loss: The learning task is defined by the loss function.
- Solver: The solver orchestrates model optimization.
- Layer Catalog: The layer serves as the fundamental unit for modeling and computation.
- Convolution
Methods and Models
- Backpropagation and modular models
- Logsum module
- RBF Net
- MAP/MLE loss
- Parameter Space Transforms
- Convolutional Module
- Gradient-Based Learning
- Energy for inference
- Objective for learning
- PCA; NLL:
- Latent Variable Models
- Probabilistic LVM
- Loss Function
- Detection with Fast R-CNN
- Sequences with LSTMs and Vision + Language with LRCN
- Pixelwise prediction with FCNs
- Framework design and future trends
Tools
- Caffe
- Tensorflow
- R
- Matlab
- Others...
Requirements
Knowledge of any programming language is required. While familiarity with Machine Learning is not mandatory, it is beneficial.
21 Hours
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
